OmniTacTune：用于视觉策略触觉残余适应的与策略无关的真实世界强化学习
OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies
AI总结：
研究针对视觉策略在接触丰富操作中不足的问题，提出OmniTacTune管道，通过残余校正让触觉反馈适应预训练视觉策略，经两阶段设计，实验证明其能跨任务等泛化，有效提升策略成功率。
摘要：
从人类视频、遥操作和机器人演示中学习的视觉策略提供了可扩展的运动先验，但在接触丰富的操作中常失败。触觉传感提供补充信号，但数据收集成本高且难泛化。我们引入OmniTacTune，通过残余校正使触觉反馈适应预训练视觉策略，经两阶段设计，实验表明其能跨多种情况泛化，有效提升成功率。
英文摘要：
Visual policies learned from human videos, teleoperation, and robot demonstrations offer scalable motion priors, but often fail in contact-rich manipulation, where success significantly depends on local force and contact geometry. Tactile sensing provides these complementary signals, yet tactile data remain costly to collect and hard to generalize across sensors, robots, and tasks. We introduce OmniTacTune, a policy-agnostic real-world RL pipeline that adapts tactile feedback to pretrained visual policies through residual correction. OmniTacTune uses a two-stage design: it first bootstraps tactile-aware learning from autonomous base-policy rollouts, then learns a lightweight tactile residual policy through online interaction. Extensive experiments show that OmniTacTune generalizes across diverse contact-rich tasks, visual base policies, and tactile representations. Across four real-world contact-rich tasks, it improves visual base policies from 5-40% success to 85-100% within 40-80 minutes, demonstrating an efficient path for adapting tactile feedback to scalable visual robot policies. Project page: https://colinyu1.github.io/omnitactune-site/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
